Unlike any of the traditional neural nets, the neural network based on ai-one, the HoloSemantic Data Space neural network (invented by Manfred Hoffleisch) or in short “HSDS”, are massively connected, asymmetrical graphs which are stimulated by binary spikes. HSDS do not have any neural structures pre-defined by the user. Their building blocks resemble biological neural networks: a neuron has dendrites, on which the synapses from other neurons are placed, and an axon which ends in synapses at other neurons.

The connections between the neurons emerge in an unsupervised manner while the learning input is translated into the neural graph structure. The resulting graph can be queried by means of specific stimulations of neurons. In traditional neural systems it is necessary to set up the appropriate network structure at the beginning according to what is to be learned. Moreover, the supervised learning employed by neural nets such as the perceptron requires that a teacher be present who answers specific questions. Even neural nets that employ unsupervised learning (like those of Hopfield and Kohonen) require a neighborhood function adapted to the learning issue. In contrast, HSDS require neither a teacher nor a predefined structure or neighborhood function (note that although a teacher is not required, in most applications programmatic teaching is used to insure the HSDS has learned the content needed to meet performance requirements). In the following we characterize HSDS according to their most prominent features.
Exploitation of context
In ai-one applications like BrainDocs, HSDS is used for the learning of associative networks and feature extraction. The learning input consists of documents from the application domains, which are broken down into segments rather than entered whole: all sentences may be submitted as is or segmented into sub-sentences according to grammatical markers. By way of experimenting, we have discovered that a segment should ideally consist of 7 to 8 words. This is in line with findings from cognitive psychology. Breaking down text documents into sub-sentences is the closest possible approximation to the ideal segment size. The contexts given by the sub-sentence segments help the system learn. The transitivity of term co-occurrences from the various input contexts (i.e. segments) are a crucial contribution to creating appropriate associations. This can be compared with the higher-order co-occurrences explored in the context of latent semantic indexing.

Continuously evolving structure
The neural structure of a HSDS is dynamic and changes constantly in line with neural operations. In the neural context, change means that new neurons are produced or destroyed and connections reinforced or inhibited. Connections that are not used in the processing of input into the net for some time will get gradually weaker. This effect can also be applied to querying, which then results in the weakening of connections that are rarely traversed for answering a query.

Asymmetric connections
The connections between the neurons need not be equally strong on both sides and it is not necessary that a connection should exist between all the neurons (cp. Hopfield’s correlation matrix).

Spiking neurons
The HSDS is stimulated by spikes, i.e. binary signals which either fire or do not. Thresholds do not play a role in HSDS. The stimulus directed at a neuron is coded by the sequence of spikes that arrive at the dendrite.

Massive connectivity
Whenever a new input document is processed, new (groups of) neurons are created which in turn stimulate the network by sending out a spike. Some of the neurons reached by the stimulus react and develop new connections, whereas others, which are less strongly connected, do not. The latter nevertheless contribute to the overall connectivity because they make it possible to reach neurons which could not otherwise be reached. Given the high degree of connectivity, a spike can pass through a neuron several times since it can be reached via several paths. The frequency and the chronological sequence in which this happens determine the information that is read from the net

General purpose
There is no need to define a topology before starting the learning process because the neural structure of the HSDS develops on its own. This is why it is possible to retrieve a wide range of information by means of different stimulation patterns. For example, direct associations or association chains between words can be found, the words most strongly associated with a particular word can be identified, etc.

Since we began the process of building applications using our AI engine, we have been focused on working with ideas or concepts. With BrainDocs we built intelligent agents to find and score similarity for ideas in paragraphs, but still fell short of the vision we have for our solution. Missing was an intuitive and visual UI to explore content interactively using multiple concepts and metadata (like dates, locations, etc). We want to give our users the power to create a rich and personal context to power through their research. What do I call this?

Some Google research led me to a great visualization and blog by David McCandless on the Taxonomy of Ideas. While the words in his viz are attributes of ideas, not the ideas themselves, it got me thinking in different ways about the problem.

If you substitute an idea (product or problem) in David’s matrix and add the dimension of time, you create a useful framework. If the idea above was “car”, then the top right might be Tesla and bottom left a Yugo (remember those?). Narrow the definition to “electric car” or generalize to “eco-friendly personal transportation” and the matrix changes. But insert an unsolved problem and now you have trouble applying the attributes. You also arrive at an innovator’s dilemma (not the seminal book by Clayton Christensen), the challenge of researching something that hasn’t been labeled and categorized yet.

Ideas begin in someone’s head. With research, debate, and engineering, they become products. Products have labels and categories that facilitate communication, search and commerce. The challenge for idea search on future problems is that the opposite occurs: products are not yet ideas and the problems they solve may not have been defined yet. If I may, Donald Rumsfeld nailed the problem with this famous quote:

“There are known knowns. These are things we know that we know. There are known unknowns. That is to say, there are things that we know we don’t know. But there are also unknown unknowns. There are things we don’t know we don’t know.”

And if it’s an unknown unknown, it certainly hasn’t been labeled yet so how do you search for it? Our CEO Walt Diggelmann used to say it this way, “ai-one gives you an answer to a question, you did not know that you have to ask….! “

Innovators work in this whitespace.

If you could build and combine different intelligent (idea) agents for problems as easily as you test different combinations of words in a search box, you could drive an interactive and spontaneous exploration of ideas. In some ways this is the gift of our intelligence. New ideas and innovation are in great part combinatorial, collaborative and stimulated by bringing together seemingly unrelated knowledge to find new solutions.

Instead of pumping everything into your brain (or an AI) and hoping the ideas pop out, we want to give you the ability to mix combinations of brains, add goals and constraints and see what you can create. Matt Ridley termed this “ideas having sex”. This is our goal for Topic-Mapper (not the sex part).

So what better place to apply this approach than to the exploration of space? NASA already created a “taxonomy of ideas” for the missions of the next few decades. In my next blog I’ll describe the demo we’re working on for the grandest of the grand challenges, human space exploration.

In the sensationally titled Forbes post, Tech 2015: Deep Learning And Machine Intelligence Will Eat The World, author Anthony Wing Kosner surveys the impact of deep learning technology in 2015. This is nothing new for those in the field of AI. His post reflects the recent increase in coverage artificial intelligence (AI) technologies and companies are getting in business and mainstream media. As a core technology vendor in AI for over ten years, it’s a welcome change in perspective and attitude.

We are pleased to see ai-one correctly positioned as a core technology vendor in the Machine Intelligence Landscape chart featured in the article. The chart, created by Shivon Zilis, investor at BloombergBETA, is well done and should be incorporated into the research of anyone seriously tracking this space.

Especially significant is Zilis’ focus on “companies that will change the world of work” since these are companies applying AI technologies to innovation and productivity challenges across the public and private sectors. The resulting solutions will provide real value through the combination of domain expertise (experts and data) and innovative application development.

This investment thesis is supported by the work of Erik Brynjolfsson and Andrew McAfee in their book “The Second Machine Age”, a thorough discussion of value creation (and disruption) by the forces of innovation that is digital, exponential and combinatorial. The impact of these technologies will change the economics of every industry over years if not decades to come. Progress and returns will be uneven in their impact on industry, regional and demographic sectors. While deep learning is early in Gartner’s Hype Cycle, it is clear that the market value of machine learning companies and data science talent are climbing fast.

This need for data scientists is growing but the business impact of AI may be limited in the near future by the lack of traditional developers who can apply them. Jeff Hawkins of Numenta has spoken out on this issue and we agree. It is a fundamentally different way to create an application for “ordinary humans” and until the “killer app” Hawkin’s speaks about is created, it will be hard to attract enough developers to invest time learning new AI tools. As the chart shows, there are many technologies competing for their time. Developers can’t build applications with buzzwords and one size fits all APIs or collections of open source algorithms. Technology vendors have a lot of work to do in this respect.

Returning to Kosner’s post, what exactly is deep learning and how is it different from machine learning/artificial intelligence? According to Wikipedia,

Deep learning is a class of machine learning training algorithms that use many layers of nonlinear processing units for feature extraction and transformation. The algorithms may be supervised or unsupervised and applications include pattern recognition and statistical classification.

are based on the (unsupervised) learning of multiple levels of features or representations of the data. Higher level features are derived from lower level features to form a hierarchical representation.

are part of the broader machine learning field of learning representations of data.

learn multiple levels of representations that correspond to different levels of abstraction; the levels form a hierarchy of concepts.

form a new field with the goal of moving toward artificial intelligence. The different levels of representation help make sense of data such as images, sounds and texts.

These definitions have in common (1) multiple layers of nonlinear processing units and (2) the supervised or unsupervised learning of feature representations in each layer, with the layers forming a hierarchy from low-level to high-level features.

While in the 4th bullet this is termed a new field moving toward artificial intelligence, it is generally considered to be part of the larger field of AI already. Deep learning and machine intelligence is not the same as human intelligence. Artificial intelligence in this definition above and in the popular press usually refers to Artificial General Intelligence (AGI). AGI and the next evolution, Artificial Super Intelligence (ASI) are the forms of AI that Stephen Hawking and Elon Musk are worried about.

This is powerful stuff no question, but as an investor, user or application developer in 2015 look for the right combination of technology, data, domain expertise, and application talent applied to a compelling (valuable) problem in order to create a disruptive innovation (value). This is where the money is over the new five years and this is our focus at ai-one.

Given the need for more effective content marketing and better quality lead generation, why aren’t the tools better? Certainly there are lots of applications, SaaS products and services available for all parts of the marketing and sales process. With BrainBrowser we provide a tool that can understand the content from marketing and match it to bloggers, LinkedIn connections, Twitter followers and find candidates in places you would never look.

Since about one-third of the 7,500+ queries by our testers were using BrainBrowser to search for people, a key objective is to add features to manage the results and integrate them into your workflow. If you find someone relevant to your work or a potential recruit, you should be able to connect with them right from the list, follow them on Twitter or share lists of candidates with collaborators.

As a recruiting professional your task is to find the candidates and conversations on the web where conversions will be maximized and get there first. BrainBrowser does this for you, creating a list of people, companies and sites that match the content of your position and company description.

As a sales professional, you want to use content, either from your marketing department or content you find and create on your own, to engage your network and to identify the people that are talking about and responsible for buying/influencing a purchase.

Nimble, a new social CRM application, has made integration easy and I’m recommending it to everyone. All you need to do is sign up for the trial (its only $15 per month if you like it) and install the plug in in your Chrome browser. You’ll then be able to highlight the name of the person on the list in BrainBrowser, right click, select the Nimble Search and a popup will display the person’s social media pages in LinkedIn, Twitter, Google+ etc. Click Save and you’ve added them to your Nimble Contacts where you can then view their social media messages, profile and decide whether to connect or follow. Tag them and you’ve creating a recruiting hot list you can track in Nimble.

Let me know how you like it. They do a great job but if you have any questions on the difference between CRM and Social CRM, and how we’re using it for recruiting. Be sure to add @ai_one or @tom_semantic if you tweet about this and sign up to request a login for BrainBrowser.

As of today, there are only 22 slots left for FREE registrations under the Alpha test program. Participation gets you a year free on the platform. Email or tweet @tom_semantic to sign up.

We are pleased to announce the availability of the following publication from prestigious ETH University in Zurich. This book will be a valuable resource to developers, data scientists, search and knowledge management educators and practitioners trying to deal with the massive amounts of information in both public and private data sources. We are proud to have our contribution to the field acknowledged in this way.

ai-one was invited to contribute as co-author to a chapter in this technical book.

In the anthology readers will find very different conceptual and technological methods for modeling and digital representation of knowledge for knowledge organizations (universities, research institutes and educational institutions), and companies based on practical examples presented in a synopsis. Both basic models of the organization of knowledge and technical implementations are discussed including their limitations and difficulties in practice. In particular the areas of knowledge representation and the semantic web are explored. Best practice examples and successful application scenarios provide the reader with a knowledge repository and a guide for the implementation of their own projects. The following topics are covered in the articles:

hypertext-based knowledge management

digital optimization of the proven analog technology of the list box

innovative knowledge organization using social media

search process visualization for digital libraries

semantic events and visualization of knowledge

ontological mind maps and knowledge maps

intelligent semantic knowledge processing systems

fundamentals of computer-based knowledge organization and integration

The book also includes coding medical diagnoses, contributions to the automated creation of records management models, business fundamentals of computer-aided knowledge organization and integration, the concept of mega regions to support of search processes and the management of print publications in libraries.

At the San Diego Business Journal Annual Innovation Award event, ai-one was named a finalist in the technology category. The award was presented at the prestigious event on June 18th at Scripps, attended by several hundred leaders in San Diego’s tech, medical, software and telecom industries. ai-one received the award for its leading edge technology in machine learning and content analytics, as evidenced by the release this year of the new Nathan API for deep learning applications.

The award was accepted by ai-one COO Tom Marsh and partner for defense and intelligence, Steve Dufour, CEO of ISC Consulting of Arizona.

ai-one was recognized for its participation in the CommNexus MarketLink event June 4th in San Diego California. The event featured companies from all across the US selected by SK Telecom for their potential to add value to SK Telecom’s network. The meeting was also attended by SK’s venture group based in Silicon Valley.

Tierney Plumb of the San Diego Daily Transcript reported, “San Diego-based ai-one inc. pitched its offerings Tuesday to the mobile operator. The company, which has discovered a form of biologically inspired neural computing that processes language and learns the way the brain does, was looking for two investments — each about $3 million — from SK. One is a next-generation Deep Personalization Project whose goal is to create an intimate personal agent while providing the user with total privacy control. ”

It has been a long time since our last blog post. Why? We’ve been busy learning how to build better intelligent agents.

Today, Kurt and I were discussing ways to improve feature detection algorithms for use in a prototype application called ai-BrainDocs. This is a system that detects concepts within legal documents. This is a hard problem because legal concepts (or ideas) use the same words. That is, there are no distinguishing features in the text.

ai-one’s technology is able to solve this problem by understanding how the same word (keyword) can mean different things by its context (as defined by association words). Together, keywords and associations create an array that we call an ai-Fingerprint. This can be thought of as a graph that can be represented as G[V,E]. ai-Fingerprints are easy to build using our Topic-Mapper API.

We pondered how the intelligent agents for Android developed by Google (called Google Now) and Apple iOS (called SIRI) might perform on a simple test. We picked a use case where the words were sparse but unique — looking for the status for a departing flight on American Airlines. Both Google Now and Apple SIRI have a tremendous advantages over ai-one because they: 1) have a lot more money to spend on R&D, 2) use expensive voice recognition technologies, and 3) they store all queries made by every user so they can apply statistical machine learning to refine results from natural language processing (NLP).

Unlike Apple and Google, ai-one’s approach is not statistical. We use a new form of artificial neural network (ANN) that detects features and relationships without any training or human intervention. This enables us to do something that Google and Apple can’t: Autonomic learning. This is a huge advantage for situations where you need to develop machine learning applications to find information where you can’t define what you are seeking. This is common in so-called “Big Data” problems. It is also much cheaper, faster and accurate than using the statistical machine learning tools that Apple and Google are pushing.